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Multidimensional Expressions (MDX) is a query language for online analytical processing (OLAP) using a database management system. Much like SQL, it is a query language for OLAP cubes. [1] It is also a calculation language, with syntax similar to spreadsheet formulae.
The MultiDimensional eXpressions (MDX) language provides a specialized syntax for querying and manipulating the multidimensional data stored in OLAP cubes. [1] While it is possible to translate some of these into traditional SQL, it would frequently require the synthesis of clumsy SQL expressions even for very simple MDX expressions. MDX has been embraced by a wide majority of OLAP vendors and has become the standard for OLAP systems.
MDX was first introduced as part of the OLE DB for OLAP specification in 1997 from Microsoft. It was invented by the group of SQL Server engineers including Mosha Pasumansky. The specification was quickly followed by commercial release of Microsoft OLAP Services 7.0 in 1998 and later by Microsoft Analysis Services. The latest version of the OLE DB for OLAP specification was issued by Microsoft in 1999.
While it was not an open standard, but rather a Microsoft-owned specification, it was adopted by a wide range of OLAP vendors.
The XML for Analysis specification referred back to the OLE DB for OLAP specification for details on the MDX Query Language. In Analysis Services 2005, Microsoft added some MDX Query Language extensions like subselects. Products like Microsoft Excel 2007 started to use these new MDX Query Language extensions. Some refer to this newer variant of MDX as MDX 2005.
In 2001 the XMLA Council released the XML for Analysis (XMLA) standard, which included mdXML as a query language. In the XMLA 1.1 specification, mdXML is essentially MDX wrapped in the XML <Statement> tag.
There are six primary data types in MDX
Aggregate (number), UniqueName (string), .Value (number or string) etc.[Time] or it can be returned by an MDX function, e.g. .Dimension. Hierarchy is a dimension hierarchy of a cube. It can be specified by its unique name, e.g. [Time].[Fiscal] or it can be returned by an MDX function, e.g. .Hierarchy. Hierarchies are contained within dimensions. (OLEDB for OLAP MDX specification does not distinguish between dimension and hierarchy data types. Some implementations, such as Microsoft Analysis Services, treat them differently.)[Time].[Fiscal].[Month] or it can be returned by an MDX function, e.g. .Level.[Time].[Fiscal].[Month].[August 2006], by qualified name, e.g. [Time].[Fiscal].[2006].[Q3].[August 2006] or returned by an MDX function, e.g. .PrevMember, .Parent, .FirstChild etc. Note that all members are specific to a hierarchy. If the self-same product is a member of two different hierarchies ([Product].[ByManufacturer] and [Product].[ByCategory]), there will be two different members visible that may need to be coordinated in sets and tuples (see below).([Time].[Fiscal].[Month].[August], [Customer].[By Geography].[All Customers].[USA], [Measures].[Sales]) or returned by an MDX function, e.g. .Item.{([Measures].[Sales], [Time].[Fiscal].[2006]), ([Measures].[Sales], [Time].[Fiscal].[2007])} or returned by MDX function or operator, e.g. Crossjoin, Filter, Order, Descendants etc.[Product].CurrentMember.[Sales Price]) or by using a special access function (for example, [Product].CurrentMember.Properties("Sales Price")). In limited contexts, MDX allows other data types as well - for example Array can be used inside the SetToArray function to specify an array that is not processed by MDX but passed to a user-defined function in an ActiveX library. Objects of other data types are represented as scalar strings indicating the object names, such as measure group name in Microsoft's MeasureGroupMeasures function or KPI name in for example Microsoft's KPIValue or KPIGoal functions.The following example, adapted from the SQL Server 2000 Books Online, shows a basic MDX query that uses the SELECT statement. This query returns a result set that contains the 2002 and 2003 store sales amounts for stores in the state of California.
SELECT{[Measures].[Store Sales]}ONCOLUMNS,{[Date].[2002],[Date].[2003]}ONROWSFROMSalesWHERE([Store].[USA].[CA])In this example, the query defines the following result set information
Note: You can specify up to 128 query axes in an MDX query.
If you create two axes, one must be the column axis and one must be the row axis, although it doesn't matter in which order they appear within the query. If you create a query that has only one axis, it must be the column axis. The square brackets around the particular object identifier are optional as long as the object identifier is not one of the reserved words and does not otherwise contain any characters other than letters, numbers or underscores.
SELECT[Measures].[Store Sales]ONCOLUMNS,[Date].MembersONROWSFROMSalesWHERE([Store].[USA].[CA])Structured Query Language (SQL) is a domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS). It is particularly useful in handling structured data, i.e., data incorporating relations among entities and variables.
Online analytical processing, or OLAP, is an approach to answer multi-dimensional analytical (MDA) queries swiftly in computing. OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging, such as agriculture.
An OLAP cube is a multi-dimensional array of data. Online analytical processing (OLAP) is a computer-based technique of analyzing data to look for insights. The term cube here refers to a multi-dimensional dataset, which is also sometimes called a hypercube if the number of dimensions is greater than three.
Essbase is a multidimensional database management system (MDBMS) that provides a platform upon which to build analytic applications. Essbase began as a product from Arbor Software, which merged with Hyperion Software in 1998. Oracle Corporation acquired Hyperion Solutions Corporation in 2007. Until late 2005 IBM also marketed an OEM version of Essbase as DB2 OLAP Server.
A user-defined function (UDF) is a function provided by the user of a program or environment, in a context where the usual assumption is that functions are built into the program or environment. UDFs are usually written for the requirement of its creator.
In computer programming contexts, a data cube is a multi-dimensional ("n-D") array of values. Typically, the term data cube is applied in contexts where these arrays are massively larger than the hosting computer's main memory; examples include multi-terabyte/petabyte data warehouses and time series of image data.
Mondrian is an open source OLAP server, written in Java. It supports the MDX (multidimensional expressions) query language and the XML for Analysis and olap4j interface specifications. It reads from SQL and other data sources and aggregates data in a memory cache.
Microsoft SQL Server Analysis Services (SSAS) is an online analytical processing (OLAP) and data mining tool in Microsoft SQL Server. SSAS is used as a tool by organizations to analyze and make sense of information possibly spread out across multiple databases, or in disparate tables or files. Microsoft has included a number of services in SQL Server related to business intelligence and data warehousing. These services include Integration Services, Reporting Services and Analysis Services. Analysis Services includes a group of OLAP and data mining capabilities and comes in two flavors multidimensional and tabular, where the difference between the two is how the data is presented. In a tabular model, the information is arranged in two-dimensional tables which can thus be more readable for a human. A multidimensional model can contain information with many degrees of freedom, and must be unfolded to increase readability by a human.
XML for Analysis (XMLA) is an industry standard for data access in analytical systems, such as online analytical processing (OLAP) and data mining. XMLA is based on other industry standards such as XML, SOAP and HTTP. XMLA is maintained by XMLA Council with Microsoft, Hyperion and SAS Institute being the XMLA Council founder members.
OLE DB for OLAP is a Microsoft published specification and an industry standard for multi-dimensional data processing. ODBO is the standard application programming interface (API) for exchanging metadata and data between an OLAP server and a client on a Windows platform. ODBO extends the ability of OLE DB to access multi-dimensional (OLAP) data stores.
Palo is a memory resident multidimensional database server and typically used as a business intelligence tool for controlling and budgeting purposes with spreadsheet software acting as the user interface. Beyond the multidimensional data concept, Palo enables multiple users to share one centralised data storage.
A database model is a type of data model that determines the logical structure of a database. It fundamentally determines in which manner data can be stored, organized and manipulated. The most popular example of a database model is the relational model, which uses a table-based format.
The following tables compare general and technical information for a number of online analytical processing (OLAP) servers. Please see the individual products articles for further information.
The following is provided as an overview of and topical guide to databases:
An array database management system or array DBMS provides database services specifically for arrays, that is: homogeneous collections of data items, sitting on a regular grid of one, two, or more dimensions. Often arrays are used to represent sensor, simulation, image, or statistics data. Such arrays tend to be Big Data, with single objects frequently ranging into Terabyte and soon Petabyte sizes; for example, today's earth and space observation archives typically grow by Terabytes a day. Array databases aim at offering flexible, scalable storage and retrieval on this information category.
BIDS Helper is a Visual Studio open source extension with multiple features that extend and enhance business intelligence development functionality in all editions of Microsoft's SQL Server 2005, 2008, 2008 R2 and 2012. BIDS Helper improves the development environment for integration, analysis and reporting services. BIDS Helper is hosted on GitHub.
Data Analysis Expressions (DAX) is the native formula and query language for Microsoft PowerPivot, Power BI Desktop and SQL Server Analysis Services (SSAS) Tabular models. DAX includes some of the functions that are used in Excel formulas with additional functions that are designed to work with relational data and perform dynamic aggregation. It is, in part, an evolution of the Multidimensional Expression (MDX) language developed by Microsoft for Analysis Services multidimensional models combined with Excel formula functions. It is designed to be simple and easy to learn, while exposing the power and flexibility of PowerPivot and SSAS tabular models.
Cubes is a light-weight open source multidimensional modelling and OLAP toolkit for development reporting applications and browsing of aggregated data written in Python programming language released under the MIT License.
The functional database model is used to support analytics applications such as financial planning and performance management. The functional database model, or the functional model for short, is different from but complementary to the relational model. The functional model is also distinct from other similarly named concepts, including the DAPLEX functional database model and functional language databases.
Kyvos is a business intelligence acceleration platform for cloud and big data platforms developed by an American privately held company named Kyvos Insights. The company, headquartered in Los Gatos, California, was founded by Praveen Kankariya, CEO of Impetus Technologies. The software provides OLAP-based multidimensional analysis on big data and cloud platforms and was launched officially in June 2015. In December the same year, the company was listed among the 10 Coolest Big Data Startups of 2015 by CRN Magazine.
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